Abstract
This paper presents the PlanMine sequence mining algorithm to extract patterns of events that predict failures in databases of plan executions. New techniques were needed because previous data miningalgorithms were overwhelmed by the staggering number of very frequent,but entirely unpredictive patterns that exist in the plan database.This paper combines several techniques for pruning out unpredictiveand redundant patterns which reduce the size of the returned rule setby more than three orders of magnitude. PlanMine has also beenfully integrated into two real-world planning systems. We experimentally evaluate the rules discovered by PlanMine, and show that theyare extremely useful for understanding and improving plans, as wellas for building monitors that raise alarms before failures happen.
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Zaki, M.J., Lesh, N. & Ogihara, M. PlanMine: Predicting Plan Failures Using Sequence Mining. Artificial Intelligence Review 14, 421–446 (2000). https://doi.org/10.1023/A:1006612804250
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DOI: https://doi.org/10.1023/A:1006612804250